Review of pseudoinverse learning algorithm for multilayer neural networks and applications

Abstract

In this work, we give an overview of pseudoinverse learning (PIL) algorithm as well as applications. PIL algorithm is a non-gradient descent algorithm for multi-layer perception. The weight matrix of network can be exactly computed by PIL algorithm. So PIL algorithm can effectively avoid the problem of low convergence and local minima. Moreover, PIL does not require user-selected parameters, such as step size and learning rate. This algorithm has achieved good application in the fields of software reliability engineering, astronomical data analysis and so on.